Diagnosing ocular-induced abnormal head posture (AHP) requires a comprehensive analysis of both head pose and ocular movements. However, existing datasets focus on these aspects separately, limiting the development of integrated diagnostic approaches and restricting AI-driven advancements in AHP analysis. To address this gap, we introduce PoseGaze-AHP, a novel 3D dataset that synchronously captures head pose and gaze movement information for ocular-induced AHP assessment. Structured clinical data were extracted from medical literature using large language models (LLMs) through an iterative process with the Claude 3.5 Sonnet model, combining stepwise, hierarchical, and complex prompting strategies. The extracted records were systematically imputed and transformed into 3D representations using the Neural Head Avatar (NHA) framework. The dataset includes 7,920 images generated from two head textures, covering a broad spectrum of ocular conditions. The extraction method achieved an overall accuracy of 91.92%, demonstrating its reliability for clinical dataset construction. PoseGaze-AHP is the first publicly available resource tailored for AI-driven ocular-induced AHP diagnosis, supporting the development of accurate and privacy-compliant diagnostic tools.
@article{arxiv.2510.03873,
title = {PoseGaze-AHP: A Knowledge-Based 3D Dataset for AI-Driven Ocular and Postural Diagnosis},
author = {Saja Al-Dabet and Sherzod Turaev and Nazar Zaki and Arif O. Khan and Luai Eldweik},
journal= {arXiv preprint arXiv:2510.03873},
year = {2025}
}
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This is a preprint version of a manuscript under review. All rights reserved by the authors